Where should we begin – again?


Creating something new is usually interesting and fun.

There are no restrictions – we’re focused on the problem at hand – and we can choose from a variety of tools to carry out the work.

Over time we develop processes and workflows that streamline things and make us more effective, especially as we add people to the project.

Things ramp up – we improve quickly – until we start to plateau.

And then we start to move more slowly – as the steps and processes and interactions start to hinder, obstruct and finally block progress.

At this point things aren’t fun any more.

At this point the friction involved in doing anything – the effort that it takes appears overwhelming.

It’s like being asked to kick a whale down the beach.

This happens in all kinds of systems – whether it’s programming, process engineering, or business strategy.

What we already have stops us from moving ahead and getting better.

So… what can we do about it?

Perhaps it starts with a mindset.

Steve Jobs, for example, was famously minimalist.

His house had hardly any furniture in it – perhaps the barest essentials – a mattress, a chest of drawers and a few folding chairs.

His philosophy led to the creation of devices like the iPad, so intuitive in design that a child can instantly use it and without the buttons and controls that others slapped on.

The problem many of us have is we have added too many buttons and features to our lives and working environments – we have too many meetings, follow too many processes and try too hard to please everyone.

What would happen if we spent that time working on interesting and useful things instead?

Perhaps we should begin by looking at everything we do and cutting back on everything that simply doesn’t help us innovate or keep customers happy.

Perfection is achieved not when there is no more to add.

It’s reached when there is no more to take away.

Why we should first do as little as possible


There are two traps we often fall into when developing something new:

  1. We try too hard to plan ahead and come up with everything we will possibly need before starting any work.
  2. We spend a lot of time making one part better and faster when it doesn’t help to make the overall performance better.

In software development the classic problem is one of requirements and specifications.

We usually don’t know what we need to do the first time we approach a problem.

It’s the process of engaging with and solving the problem usually helps clarify thinking – and when we have finished with our first solution we now know what we really need to do to solve the problem.

The idea of right first time simply doesn’t work when we don’t know what the right way is yet.

A better approach is to prototype our way to the right solution.

We ought to start small and simple – designing something we and others can understand.

Whether it’s a business process or a software program, once we can show others a working prototype we will be able to see reactions, get feedback and other useful information which can help us refine our ideas.

It’s also important we don’t get too fancy or try to locally optimise things.

Something very clever right now may be hard to understand in three year’s time by someone else, who might decide to throw out all our work and start again instead.

Local optimisation is the idea that we work very hard on speeding up one part of the process but miss the real bottlenecks in the system.

The overall time any system takes to work will be controlled by its slowest component. Making any other part work faster will not reduce the overall time.

For example, it doesn’t matter how fast we get ready in the mornings if the real bottleneck is how long it takes the bus to get to our stop.

We could be 20 minutes early – but it’s the bus time that will decide whether we are late or on time to work.

Our natural tendency, very often, is to try and do more – plan more, think more and have more.

In reality that leads to bloat – creating large unwieldy systems no one loves.

We would be better off following a few simple rules:

  1. Prototype then polish. Get it working first, then try and make it better.
  2. Doing nothing can be very constructive. Words that aren’t there can’t be read wrong. Code that isn’t written can’t fail.

If we must do something – then we should concentrate our time on the things that deliver exponential results, not incremental improvements.

Why we focus on our concerns instead of on users’ needs


Why is it that many systems – software applications, company policies, operational processes – are hard to use?

Take websites for example. How many company websites are structured in an easy to use, logical manner?

If we ask any company’s marketing team, they will probably say that their website is structured, laid out and easy to follow.

For them, that is.

This is an example of a principle called Conways’s law, which says that Any organization that designs a system (defined broadly) will produce a design whose structure is a copy of the organization’s communication structure.

In other words, the way in which we structure ourselves influences the systems we create.

For example, if we have two factories, one making bread and the other making jam, we are likely to advertise ourselves as having two product lines, the bread line and the jam line.

We’re unlikely to combine them and pitch ourselves to customers as the jam sandwich line – even though they might actually be looking for a sandwich rather than a collection of components.

This is a natural way for an organisation to behave – after all the way they look at themselves seems natural and normal – so why not show themselves to the world in that way?

Except to a user with little experience, it’s hard to work out what companies do, so they will pick the one that makes it easiest for them to understand.

The problem gets worse as we try and create bigger and bigger systems.

For example, a large software application that tries to do everything will find itself bogged down as it grows, constrained by previous decisions and choices about architecture and design.

One solution to this problem is to limit the size of teams – use multi-skilled individuals in small teams that have end-to-end responsibility for a product.

This is the principle behind work cells in lean manufacturing.

These teams tend to produce work that has clean edges and plays nicely with other teams.

Small teams are also fast – they can get together, come up with a plan, develop a product, test it and get feedback, and iterate and improve in the time it takes for a large organisation to do the paperwork needed to get permission to start.

Although, the size of teams is not a solution by itself – it simply produces systems that may be smaller, simpler and work more independently, and so be easier to scale.

The point is that our default approach when trying to design anything is to focus on what we think a user needs.

But, that thinking focuses on us – not on the user – who might actually approach the situation from a completely different point of view.

Perhaps that’s why some of the most useful tools have been developed by people who made them for their own use – they knew exactly what they wanted from the start.

How to create small changes that make a big difference


If we want to change our behaviour or influence the behaviour of a group of people around us for the better, what should we do?

In Inside The Nudge Unit, David Halpern describes how the Behavioural Insights Team in the UK government showed that designing policy using behavioural insights dramatically improves results and outcomes.

If we’re trying to do something – for example encourage more people to use public transport or corporations to invest in energy efficiency – we can use a simple framework developed by the Nudge unit to design a programme.

The framework can be remembered with a mnemonic – EAST – which stands for easy, attract, social and timely.

We need to start by making things Easy.

Just as water flows downhill, people are more likely to do something if it’s simple or the default option.

Anything that adds friction reduces performance – so we can remove friction to make things easier, or add it to make things harder.

For example, supermarkets now keep healthy snacks closer to checkout and sweets further away so that buyers find it easier to choose a healthy option rather than an unhealthy one.

Asking people to turn off the lights or the tap when they leave requires an action from them.

Making it the default through a sensor and switch or taps that open for a preset amount of time makes this easier.

Then we need to get their attention – attract them.

We can do this if we personalise information, make key points obvious, use trusted, authoritative or well-known people to publicise information and create incentives for them to act.

The key thing is getting people to have an emotional connection with the idea.

We are also more likely to do things if we see other people doing them – we are social creatures.

We look around us for guidance and confirmation that what we are doing is the right thing to do.

In many organisations, recycling is now the norm with segregated bins for different kinds of waste.

We need to make use of networks to reach out and use social recognition – awards, committments, promises – as ways to engage and enthuse people.

For example, people are much more likely to recycle when they see other people also doing it. Conversely, if they see others littering, they are more likely to do that too.

Finally, interventions work best when they are timely.

For example, the best time to work with organisations to improve energy efficiency is to engage with them at the point they are making new purchases.

That is when they can compare the purchase costs of different pieces of kit to the lifetime costs of owning and operating the kit.

If they can see that they save money over the long term at that point, then the are more likely to go for the better, more expensive option, rather than going for a cheap thing now that is more expensive and wasteful over the long term.

When we’re planning a change, whether at an individual or organisational level, we need to look at our plans and see how things might work out.

We are much more likely to be successful if we can tick off the four elements of the EAST framework.

What is our first reaction when we see someone or something?


We have a rapid and largely unconscious way of judging things and people we see for the first time.

Susan Fiske, a Professor of Psychology at Princeton, came up with the Stereotype Content Model (SCM).

This model says that we look at people and things and assess them along two axes.

Along one axis is how warm or cold we feel towards them.

Along the other axis, do they seem competent or less competent.

As the name of the model suggests, it predicts our stereotypical reaction to people, groups and things.

These reactions have developed over time as we evolved – and help us decide whether there is a threat to us or not.

For example, we perceive many social groups as warm and competent – these are friends, friends of friends, people we meet in offices wearing suits giving us presentations and so on.

They are not seen as a threat, and so we have a fairly positive reaction to them.

On the other hand, what if it’s a social community that we don’t know very well but which controls a section of business or trade.

Or perhaps it’s an aloof and wealthy owner of an organisation employing many people.

In those cases, we may respect the community or person as competent, but feel a coolness towards them, driven apart by differences in culture or status.

On the other side, we may feel warmth towards an elderly person but be less convinced about their competence.

The way we feel, however, may mean we help them cross the road, with their luggage or go to their aid when something is wrong.

A cold reaction may kick in and we hurry past if we see a homeless person holding a bottle – that could be seen as a threat and we don’t stop to get involved.

We get the same kinds of feelings when we look at things – like cars, for example.

A Mercedes going past us on the street may seem cold and aloof but a Camper van seems open and welcoming.

So, what does this mean for us?

The first reactions we have are quite likely to affect the way we act.

For example, feeling less positive about a vulnerable group will lessen our willingness to give.

Seeing exercise as something we are less competent at will drain our motivation quickly.

What we want to do is create more associations with things that are warm and competent – if we see them as positive and fulfilling we are likely to engage more and persist longer.

This attitude alone might make the difference between success and failure.

What would you do if the internet went down?


It’s a question of when, not if, a major cyber-attack will happen according to Ciaran Martin, the head of the UK’s National Cyber Security Centre.

The number of attacks keeps increasing and they target different levels of equipment – from personal computers and the servers that keep the network operating to the internet-connected devices that keep power plants running.

If it’s likely that we will experience an attack, then we need to think about the impact it will have on the things we do to figure out how to respond.

For example, the first things to go down are often the major websites – the news channels, search engines and social media platforms.

We are instantly more isolated – we don’t realize how much we depend on communicating through the internet these days until it goes down.

With the networks down, the people most at risk are vulnerable ones on their own – do governments have contingency plans to ensure their safety?

Some attacks are untargeted – affecting people by infecting the systems they use, while others are targeted against specific companies or sectors.

The UK energy sector, for example, is classed as critical infrastructure and is regularly attacked as part of an ongoing campaign of espionage.

As we move to a decentralized system of energy generation and usage – with the devices that make energy like solar panels and the devices that use energy like fridges and washing machines all connected – we dramatically increase the number of devices that can be attacked or used in attacks.

That means the security processes we follow need to apply to more than just our computers but to the rest of the devices we have – and traditionally that level of security has been overlooked.

We also need to think about the information associated with these systems and where we should keep it.

For example, with the increasing use of cloud computing, we need to have access to the internet to use many services and access data.

Cloud computing specialists, in theory, should be better prepared and have the security systems and people in place to manage risks and stop attacks.

But can we trust them? Uber hid a breach and paid hackers who stole the information of 57 million of its users and drivers.

If the information was kept on a local server, however, what greater assurances do we have that it’s safe from a determined hacker?

It’s clear that there is a threat to us. And we need to be prepared for it.

As the old proverb goes – forewarned is forearmed.

The secret to making money


Every real businessperson I have met thinks about money in a certain way.

They break things down into what they cost on a daily or weekly basis and then see how much they need to make every day or week to be in profit.

For example, let’s say we wanted to start a taxi service and bought a car for £10,000 that will be run for 3 years – and it’s going to be worth £3,000 at the end.

A business person will think of that as £7,000 of capital spread over three years to make decisions.

The taxi might be in service 5 days a week, 48 weeks of the year for 3 years – that’s 720 days of operation.

So we need to make at least £10 a day to pay for the car. The stuff on top is profit.

It’s as simple as that – once we are making a profit, however, lots of people will want a bite of it – from the government to helpers.

But the basic principle is still just that simple.

Sometimes the more complex financial calculations that we do – from paybacks to internal rate of return – simply confuse the issue.

Especially when it comes to serious projects that have lots of moving parts.

For example, battery storage plus solar is all the rage now.

Around a year ago I made some notes from a podcast by Barry Cinnamon, who uses exactly this method to evaluate the economics of an battery storage plus solar installation.

It’s from 2016, and prices may have changed, but principles don’t.

We are currently going through an infrastructure upgrade process in the UK that staggering in its scope and scale.

We have ageing networks of wires, pipes, sewers, roads and railways, all of which are being upgraded or replaced.

And there are a myriad projects being chased by developers.

For projects in the energy business – it’s once again pretty simple.

There is a price per kWh of energy – and if we can buy it for less or sell it for more than that we can make a profit.

The problem is when we have very complex financial models that help us buy it for more or sell it for less.

As Theresa May (now probably wishing she hadn’t) said, there is no magic money tree.

But there are many, very real, money pits.

What types of data analysis can we do?


We live in a world where we collect increasing amounts of data – but how many of us do anything with it?

At one extreme, we might do nothing at all, missing out on insights that could make a difference to the way in which we live and work.

At the other, we could create very detailed and sophisticated analyses that either no one understands or work under such specific conditions that they are not terribly useful.

The data out there includes information on customer behaviour, sales activity, operational production, energy use, waste generation – the list seems endless.

So, what can we do about improving the way we go about analysing the data?

An approach summarized by Dr. Jerry A. Smith and attributed to Jeffrey Leek suggests six types of analysis we can do.

We can start by describing the data – finding out more about its shape and characteristics.

How large is the data set? What is the average value? What does the distribution look like? Are there any outliers?

A large number of analyses stop here and go no further.

But what we should do next is explore the data. This means that we look for relationships between variables.

How does one variable correlate with another, or change over time?

For example, a classic use case is to look at energy consumption in relation to the outside air temperature.

Google correlate is an interesting tool that lets us see what kinds of search patterns match real world data.

The warning, as always, is correlation is not causation.

Our next, cautious, step is to see if we can infer something from our analysis to date.

Given what we have learned, can we say something about what might happen more widely?

So given the reactions of a sample of customers, can we be reasonably confident that the wider market will react in a certain way?

The level of certainty we have will make this method flow into the next as we predict what will happen.

At election time, this kind of data analysis always reaches fever pitch. All night analyses, updated with constant data feeds, update and predict the outcome.

Prediction is usually possible over relatively short time scales – we might be able to predict with accuracy the winner of a presidential contest in the next month, but not the winner from a pool of potential candidates five years from now.

Now we have to see if there is a causal relationship between two variables – a change in one will cause a specific change in the other.

This is the kind of analysis that happens in clinical trials. For example, a specific dose of a drug will result in a measurable improvement in a condition.

Finally we can look at a mechanistic analysis or an exact model, where we know what will happen as all the variables change.

This is usually the domain of engineering models – we know that a steam train will operate in a certain way once the water gets up to temperature and pressure and the various mechanical systems start to operate.

In a sense, the various methods of analysis progress from simple to complex.

A complex system – like human beings in a social environment – may only be understood with simpler analyses.

We can predict what someone will do, but we cannot say with certainty that a particular set of input stimuli will cause exactly certain neurons to fire and result in a defined activity.

An exact model may only be possible with engineering systems that operate within clear parameters and tolerances.

Analysing data isn’t something that comes naturally to most people.

We need to work on developing the skills, capabilities and toolkit needed to make sense of data.

And that probably starts with knowing what types of analyses we can do and understanding the situations where we can apply them.

Which business model will catch the next wave?


Charlie Munger talked about competitive destruction – the process by which new businesses come along and destroy older ones – often built using new and different technology.

Being one of the first to market can be a good thing in this situation.

Using a surfing model, if a business can get up and catch the wave, they could ride it for a long time, making profits on the way.

Intel did it with microprocessors, Microsoft with desktop operating systems, Apple with smartphones and Google with search.

Products based on artificial intelligence (AI) and machine learning might seem good candidates for the next wave.

Take the way in which we use mobile phones, for instance.

Tools like predictive text have been around for a while – but phones are used for much more than talking or texting.

Navigation systems on them have gone from route planning to real time route optimisation, with suggestions on how to change routes in the middle of a journey based on travel patterns in the area.

Translation is another area being transformed by technology.

For gist translation – where what we need is an understanding of what a document says in a different language – the systems built into browsers and search engines do a remarkable job.

Machine learning may provide the solution to spam emails.

Microsoft Outlook’s clutter service means that virtually all spam type emails are filtered out and never hit the inbox.

Generic newsletter, marketing and sales emails simply can’t interrupt us any more.

Some of us don’t worry about scheduling or planning things – the entries turn up in our diaries and we can rely on our phones to tell us where we are going and when to set off.

These tiny changes to the way in which machines help to organise and optimise our days are happening in a barely recognizable way.

But they are becoming also becoming an inextricable part of how we go about our daily business.

These changes signal a groundswell that is expected to turn into a tidal wave as AI affects everything from law and medicine to transportation and sustainability.

The question that individuals and organisations need to consider is how they will fit into a world where work requires hybrid human-machine skills.

Should we go for the easy option?


Warren Buffett wrote that after many years he and his partner, Charlie Munger, had not learned to solve business problems.

What they had learned to do was to avoid them, by looking for one-foot hurdles they could step over rather than seven-foot ones they needed to clear.

But how can this approach be applied day to day?

Take the emerging field of product management.

Is it better to create a new product and then try and sell it to potential users or to first try and understand the needs of potential users and then try and design an offering around those?

One school of thought argues that customers don’t know what they need before they see the product – if you had asked people what they wanted before the car was invented, they might have said a faster horse.

If the business we’re in is more humdrum – more exposed to competition – what approach can we take?

Let’s say we owned a food business – what advantages would help us beat the competition? Would it be better ingredients, better signage, widespread advertising or more delivery options?

The late Gary Halbert used this example and said people could choose any combination of advantages they wanted and he would still beat them if he had a single advantage – A starving crowd.

The test for any product is not how good it is or how glowing the reviews are – it’s how well it’s doing on gaining market share.

The energy efficiency business, for example, should really be an easy one to operate in.

Who wouldn’t want to cut their energy costs – after all savings go straight to the bottom line and how much product would a company need to sell to get the same result?

But many projects fail to go ahead because they don’t meet a 2-year payback?

But, if project developers thought like product managers, they might think about what the CEO and FD of the company really want to achieve.

If they are like most CEOs and FDs, their focus is on earnings growth and increasing shareholder value.

Payback to them is less important than what the project will contribute to EBITDA during its lifetime.

A McKinsey article shows how a modern approach to a portfolio of projects might evaulate them all, rank them on a risk/reward basis and select the ones close to the efficient frontier – essentially the best ones.

Cherry-picking makes it more likely that investments will return value in the long term.

We are often programmed to believe that anything worth doing must be hard – taking effort and sacrifice.

By going after the easy things, however, we may actually make a difference and create value.

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